rm(list=ls())
set.seed(1234)
library(car)
library(plyr)
library(ggplot2)
library(psycho)
library(texreg)

setwd(".../Data/Raw")

modeldata<-read.csv("USA.csv")

##create treatment number 


modeldata$Vignettes_DO_treat1 <-recode(modeldata$Vignettes_DO_treat1, " 1 = 1; else=0")
modeldata$Vignettes_DO_treat2 <-recode(modeldata$Vignettes_DO_treat2, " 1 = 2; else=0")
modeldata$Vignettes_DO_treat3 <-recode(modeldata$Vignettes_DO_treat3, " 1 = 3; else=0")
modeldata$Vignettes_DO_treat4 <-recode(modeldata$Vignettes_DO_treat4, " 1 = 4; else=0")
modeldata$Vignettes_DO_treat5 <-recode(modeldata$Vignettes_DO_treat5, " 1 = 5; else=0")
modeldata$Vignettes_DO_treat6 <-recode(modeldata$Vignettes_DO_treat6, " 1 = 6; else=0")

#merge treatments to create one variable 

modeldata$treat<-modeldata$Vignettes_DO_treat1 +  modeldata$Vignettes_DO_treat2 + modeldata$Vignettes_DO_treat3 + modeldata$Vignettes_DO_treat4 + modeldata$Vignettes_DO_treat5 + modeldata$Vignettes_DO_treat6

table(modeldata$treat)

modeldata$treat<-recode(modeldata$treat, " 0 = NA")

#create sub leg and pro leg standardized scores 

modeldata$decide_citizen <-as.character(modeldata$decide_citizen)
modeldata$decide_women <-as.character(modeldata$decide_women)
modeldata$decide_animal <-as.character(modeldata$decide_animal)
modeldata$fair_women <-as.character(modeldata$fair_women)
modeldata$fair_animal <-as.character(modeldata$fair_animal)
modeldata$decisionprocess <-as.character(modeldata$decisionprocess)
modeldata$overturn <-as.character(modeldata$overturn)
modeldata$council_trust <-as.character(modeldata$council_trust)
modeldata$personalcouncil <-as.character(modeldata$personalcouncil)

modeldata[modeldata =="Very unfair"]<-1
modeldata[modeldata =="Unfair"]<-2
modeldata[modeldata =="Somewhat fair"]<-3
modeldata[modeldata =="Fair"]<-3
modeldata[modeldata =="Very fair"]<-4

modeldata[modeldata =="Strongly disagree"]<-1
modeldata[modeldata =="Disagree"]<-2
modeldata[modeldata =="Agree"]<-3
modeldata[modeldata =="Strongly agree"]<-4
modeldata[modeldata =="Strongly Agree"]<-4

modeldata$decide_citizen<-as.numeric(as.character(modeldata$decide_citizen))
modeldata$decide_women <-as.numeric(as.character(modeldata$decide_women))
modeldata$decide_animal <-as.numeric(as.character(modeldata$decide_animal))
modeldata$fair_women <-as.numeric(as.character(modeldata$fair_women))
modeldata$fair_animal <-as.numeric(as.character(modeldata$fair_animal))
modeldata$decisionprocess <-as.numeric(as.character(modeldata$decisionprocess))
modeldata$overturn <-as.numeric(as.character(modeldata$overturn))
modeldata$council_trust <-as.numeric(as.character(modeldata$council_trust))
modeldata$personalcouncil <-as.numeric(as.character(modeldata$personalcouncil))

#reverse code overturn, so higher values represent higher legitimacy views

modeldata$overturnR<-recode(modeldata$overturn, " 1=4; 2=3; 3=2; 4=1")

#subset the data for the issue area of sexual harassment 

harass<-subset(modeldata, treat == 1 | treat == 2 | treat == 3 )

##sexual harassment vignettes (treat 1 = AMP, treat 2 = GBP, treat 3 = Q-GBP)

#subset the data to those that pass the manipulation checks
harass<-subset(harass, harass$decide_harass=="Require sexual harassment training")

#substantive legitimacy

factor1<-omega(harass[,c(51:52, 54)], nfactors=1) #alpha = 0.86
harass$SubLeg<-factor1$scores[,1]
harass$SubLegStand<-standardize(harass$SubLeg)

#procedural legitimacy

factor1<-omega(harass[,c(56, 82, 58)], nfactors=1) #alpha = 0.83
harass$ProLeg<-factor1$scores[,1]
harass$ProLegStand<-standardize(harass$ProLeg)

harass <- harass[!is.na(harass$SubLegStand),] #remove NA values
harass <- harass[!is.na(harass$ProLegStand),] #remove NA values

##subset the animal mistreatment vignettes 

animal<-subset(modeldata, treat == 4 | treat == 5 | treat == 6)

#Create indicies for three treatment groups 

#substantive legitimacy
factor1<-omega(animal[,c(51,53, 55)], nfactors=1) #alpha = 0.87
animal $SubLeg<-factor1$scores[,1]
animal $SubLegStand<-standardize(animal $SubLeg)

#procedural legitimacy 
factor1<-omega(animal[,c(56, 82, 58)], nfactors=1) #alpha = 0.72
animal $ProLeg<-factor1$scores[,1]
animal $ProLegStand<-standardize(animal $ProLeg)

animal <- animal[!is.na(animal $SubLegStand),] #remove NA values from DV
animal <- animal[!is.na(animal $ProLegStand),] #remove NA values from DV

#Create indicies and plot for three treatment groups 

USAgg<-rbind(harass[, c(81, 84, 86, 18, 20)], animal[, c(81, 84, 86, 18, 20)]) 
USAgg $Country <-rep("USA", nrow(USAgg))

write.csv(USAgg, "USAgg.csv")



